mlsanityData to 7 Oct 2026

Parameterized Machine Learning for High-Energy Physics

arXiv 1601.07913 ↗First posted 28 Jan 2016hep-ex cs.LG hep-phcited by 33

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Abstract (arXiv)

We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2016.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 4; 2024: 5; 2025: 6; 2026: 5.

33 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2023Subtractive Mixture Models via Squaring: Representation and Learningcited by 39
  3. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  4. 2024Sum of Squares Circuitscited by 21
  5. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  6. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  7. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  8. 2024Uncertainty Quantification of Surrogate Models using Conformal Predictioncited by 11
  9. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  10. 2021Autoencoders for Semivisible Jet Detectioncited by 9
  11. 2022The Quarks of Attentioncited by 7
  12. 2021Inferring dark matter substructure with astrometric lensing beyond the power spectrumcited by 4
21 more
  1. 2024Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Productioncited by 4
  2. 2022Machine-Learning Compression for Particle Physics Discoveriescited by 3
  3. 2023Dual input neural networks for positional sound source localizationcited by 3
  4. 2025Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physicscited by 3
  5. 2021Deconvolution-and-convolution Networkscited by 2
  6. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  7. 2025Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimationcited by 2
  8. 2022Improving Parametric Neural Networks for High-Energy Physics (and Beyond)cited by 1
  9. 2025Discriminative versus Generative Approaches to Simulation-based Inferencecited by 1
  10. 2026Unknown Unknowns: Model Misspecification in Machine Learning for Physicscited by 1
  11. 2021Knothe-Rosenblatt transport for Unsupervised Domain Adaptationcited by 0
  12. 2022Block size estimation for data partitioning in HPC applications using machine learning techniquescited by 0
  13. 2023Topological Learning in Multi-Class Data Setscited by 0
  14. 2024Modeling Sampling Distributions of Test Statistics with Autogradcited by 0
  15. 2025Generator Based Inference (GBI)cited by 0
  16. 2025Increasing Information Extraction in Low-Signal Regimes via Multiple Instance Learningcited by 0
  17. 2025Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experimentscited by 0
  18. 2026A lift for input-convex neural net trainingcited by 0
  19. 2026In-Context Multiple Instance Learningcited by 0
  20. 2026Physics-Informed Classical and Quantum Neural Networks for One-Dimensional Schrodinger Eigenvalue Problemscited by 0
  21. 2026Meta-learning accelerates detector design optimizationcited by 0